Multi-task Learning for License Plate Recognition in Unconstrained Scenarios

The recognition of license plates in natural scenes often face challenges such as multi-directional and multi-line variations. Additionally, previous studies have treated license plate detection and recognition as separate tasks, resulting in inefficiencies and error accumulation. To address these challenges, we propose an end-to-end method for license plate detection and recognition using multi-task learning. Firstly, we introduce two parallel branches to detect the horizontal bounding box and the four corners of the license plate, enabling multi-directional license plate detection in a multi-task manner. The outputs from these branches are combined to enhance recognition accuracy. Secondly, we propose to extract global features to perceive character layout and utilize reading order to spatially attend to characters for recognizing multi-line license plates. Finally, we combine detection and recognition using the same backbone, with the detection branch based on multiple deep layers and the recognition branch based on multiple shallow layers, thereby constructing an end-to-end detection and recognition network. Comparative experiments on CCPD and RodoSol datasets validate that our method significantly outperforms state-of-the-art methods, particularly in scenarios involving multi-directional and multi-line license plates.